{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from glob import glob\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_stats_per_conv(messages):\n",
    "    # Remove the initial message (prompt)\n",
    "    assert messages[0][\"role\"] == \"user\"\n",
    "    messages = messages[1:]\n",
    "    try:\n",
    "        # If last message is \"assistant\", \"Answer\" -> Add one user message\n",
    "        if messages[-1][\"role\"] == \"assistant\" and \"Answer\" in messages[-1][\"content\"]:\n",
    "            messages += [{\"role\": \"user\", \"content\": \"Success\"}]\n",
    "        assert messages[-1][\"role\"] == \"user\", f\"Last message is not user: {messages[-1]}\"\n",
    "        user_messages_content = [\n",
    "            m[\"content\"] for m in messages if m[\"role\"] == \"user\"\n",
    "        ]\n",
    "        n_turns = len(user_messages_content)\n",
    "        # Syntax Incorrect (Initial Action/Answer:): \"Invalid generation. \"\n",
    "        n_syntax_incorrect_keywords = sum([\n",
    "            \"Invalid generation.\" in m for m in user_messages_content\n",
    "        ])\n",
    "        # Syntax Incorrect (Issue with tool-call): \"Invalid action. \"\n",
    "        n_syntax_incorrect_tool_calls = sum([\n",
    "            \"Invalid action.\" in m for m in user_messages_content\n",
    "        ])\n",
    "        # Calculate Partial Syntax Correct : \"*Extra reminder: \" in \"user\" content\n",
    "        n_partial_syntax_correct = sum([\n",
    "            \"*Extra reminder:\" in m for m in user_messages_content\n",
    "        ])\n",
    "\n",
    "        return {\n",
    "            \"n_turns\": n_turns,\n",
    "            \"n_syntax_incorrect_keywords\": n_syntax_incorrect_keywords,\n",
    "            \"n_syntax_incorrect_tool_calls\": n_syntax_incorrect_tool_calls,\n",
    "            \"n_partial_syntax_correct\": n_partial_syntax_correct\n",
    "        }\n",
    "    except Exception as e:\n",
    "        print(messages)\n",
    "        raise e\n",
    "\n",
    "\n",
    "def analyze_dir(dir_names, prefix):\n",
    "    model_to_df = defaultdict(list)\n",
    "\n",
    "    for dir_name in dir_names:\n",
    "        dir_name = prefix + dir_name\n",
    "        model_dirs = os.listdir(dir_name)\n",
    "        model_dirs = [d for d in model_dirs if os.path.isdir(os.path.join(dir_name, d))]\n",
    "\n",
    "        for model_name in model_dirs:\n",
    "            # print(model_name)\n",
    "            try:\n",
    "                text_as_action_df = pd.read_json(os.path.join(dir_name, model_name, 'text_as_action.json'), lines=True)\n",
    "                json_as_action_df = pd.read_json(os.path.join(dir_name, model_name, 'json_as_action.json'), lines=True)\n",
    "                code_as_action_df = pd.read_json(os.path.join(dir_name, model_name, 'code_as_action.json'), lines=True)\n",
    "            except:\n",
    "                print(f\"**Skipping {model_name} since it doesn't have all the files\")\n",
    "                continue\n",
    "\n",
    "            df = pd.concat([text_as_action_df, json_as_action_df, code_as_action_df])\n",
    "\n",
    "            if \"messages\" not in df.columns:\n",
    "                print(f\"**Skipping {model_name} since it doesn't have messages\")\n",
    "                print(df)\n",
    "                continue\n",
    "            try:\n",
    "                conv_stats = df[\"messages\"].apply(calculate_stats_per_conv).apply(pd.Series).rename(\n",
    "                    columns={\"n_turns\": \"n_turns_conv\"}\n",
    "                )\n",
    "            except Exception as e:\n",
    "                print(model_name, dir_name)\n",
    "                raise e\n",
    "            # merge the stats\n",
    "            df = pd.concat([df, conv_stats], axis=1)\n",
    "            assert (df[\"n_turns_conv\"] == df[\"n_turns\"]).all()\n",
    "            df = df.drop(columns=[\"n_turns_conv\"])\n",
    "            model_to_df[model_name].append(df)\n",
    "\n",
    "    model_stats = []\n",
    "    output_df = []\n",
    "    # Concatenate all the dataframes\n",
    "    for model_name, dfs in model_to_df.items():\n",
    "        df = pd.concat(dfs)\n",
    "        _grouped = df.groupby([\"is_single_tool_task\", \"action_mode\"])\n",
    "        df[\"model_name\"] = model_name\n",
    "        output_df.append(df)\n",
    "\n",
    "        stats = pd.concat([\n",
    "                _grouped[\"n_turns\"].agg([\"mean\", \"count\", \"sum\"]).rename(\n",
    "                    columns={\"mean\": \"avg_turns\", \"count\": \"n_tasks\", \"sum\": \"n_total_turns\"}\n",
    "                ),\n",
    "                _grouped[\"is_correct\"].mean().to_frame(),\n",
    "                _grouped[[\"n_syntax_incorrect_keywords\", \"n_syntax_incorrect_tool_calls\", \"n_partial_syntax_correct\"]].sum()\n",
    "            ],\n",
    "            axis=1\n",
    "        )\n",
    "        stats[\"model_name\"] = model_name\n",
    "        stats = stats.reset_index().set_index([\n",
    "            \"is_single_tool_task\", \"model_name\", \"action_mode\"\n",
    "        ]).sort_index()\n",
    "        stats[\"pct_syntax_incorrect_keywords\"] = stats[\"n_syntax_incorrect_keywords\"] / stats[\"n_total_turns\"]\n",
    "        stats[\"pct_syntax_incorrect_tool_calls\"] = stats[\"n_syntax_incorrect_tool_calls\"] / stats[\"n_total_turns\"]\n",
    "        stats[\"pct_partial_syntax_correct\"] = stats[\"n_partial_syntax_correct\"] / stats[\"n_total_turns\"]\n",
    "        stats[\"pct_syntax_correct\"] = 1 - stats[\"pct_syntax_incorrect_keywords\"] - stats[\"pct_syntax_incorrect_tool_calls\"] - stats[\"pct_partial_syntax_correct\"]\n",
    "        stats = stats[[\n",
    "            \"n_tasks\", \"avg_turns\", \"is_correct\",\n",
    "            \"pct_syntax_incorrect_keywords\", \"pct_syntax_incorrect_tool_calls\", \"pct_partial_syntax_correct\", \"pct_syntax_correct\"\n",
    "        ]].rename(columns={\n",
    "            \"is_correct\": \"Correct %\",\n",
    "            \"pct_syntax_incorrect_keywords\": \"Syntax Incorrect (Initial Action/Answer) %\",\n",
    "            \"pct_syntax_incorrect_tool_calls\": \"Syntax Incorrect (Issue with tool-call) %\",\n",
    "            \"pct_partial_syntax_correct\": \"Partial Syntax Correct %\",\n",
    "            \"pct_syntax_correct\": \"Syntax Correct %\"\n",
    "        })\n",
    "\n",
    "        model_stats.append(stats)\n",
    "\n",
    "    stats = pd.concat(model_stats)\n",
    "    stats[\"avg_turns\"] = stats[\"avg_turns\"].round(1)\n",
    "    stats[\"Correct %\"] = stats[\"Correct %\"].round(3)\n",
    "    display(stats[[\"avg_turns\", \"Correct %\", \"n_tasks\"]]\\\n",
    "            .sort_index()\\\n",
    "            .style.format({\n",
    "                \"avg_turns\": \"{:.1f}\",\n",
    "                \"n_tasks\": \"{:.0f}\",\n",
    "                \"Correct %\": \"{:.1%}\"\n",
    "            })\\\n",
    "            .background_gradient(cmap='Blues', subset=[\"avg_turns\", \"Correct %\", \"n_tasks\"]))\n",
    "\n",
    "    output_df = pd.concat(output_df)\n",
    "    return stats, output_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_3ff66_row0_col0, #T_3ff66_row1_col0, #T_3ff66_row13_col0, #T_3ff66_row14_col0, #T_3ff66_row19_col0, #T_3ff66_row47_col0, #T_3ff66_row49_col0 {\n",
       "  background-color: #083b7c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row0_col1, #T_3ff66_row1_col1, #T_3ff66_row2_col1, #T_3ff66_row6_col1, #T_3ff66_row48_col1, #T_3ff66_row49_col1 {\n",
       "  background-color: #eaf3fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row0_col2, #T_3ff66_row1_col2, #T_3ff66_row2_col2, #T_3ff66_row3_col2, #T_3ff66_row4_col1, #T_3ff66_row4_col2, #T_3ff66_row5_col1, #T_3ff66_row5_col2, #T_3ff66_row6_col2, #T_3ff66_row7_col2, #T_3ff66_row8_col2, #T_3ff66_row9_col1, #T_3ff66_row9_col2, #T_3ff66_row10_col1, #T_3ff66_row10_col2, #T_3ff66_row11_col1, #T_3ff66_row11_col2, #T_3ff66_row12_col2, #T_3ff66_row13_col2, #T_3ff66_row14_col2, #T_3ff66_row15_col1, #T_3ff66_row15_col2, #T_3ff66_row16_col2, #T_3ff66_row17_col2, #T_3ff66_row18_col1, #T_3ff66_row18_col2, #T_3ff66_row19_col2, #T_3ff66_row20_col2, #T_3ff66_row21_col2, #T_3ff66_row22_col2, #T_3ff66_row23_col2, #T_3ff66_row24_col2, #T_3ff66_row25_col2, #T_3ff66_row26_col2, #T_3ff66_row27_col2, #T_3ff66_row28_col2, #T_3ff66_row29_col2, #T_3ff66_row30_col2, #T_3ff66_row31_col2, #T_3ff66_row32_col2, #T_3ff66_row33_col2, #T_3ff66_row34_col2, #T_3ff66_row35_col2, #T_3ff66_row36_col2, #T_3ff66_row37_col2, #T_3ff66_row38_col2, #T_3ff66_row39_col0, #T_3ff66_row39_col2, #T_3ff66_row40_col2, #T_3ff66_row41_col2, #T_3ff66_row42_col2, #T_3ff66_row43_col2, #T_3ff66_row44_col2, #T_3ff66_row45_col2, #T_3ff66_row46_col2, #T_3ff66_row47_col2, #T_3ff66_row48_col2, #T_3ff66_row49_col2, #T_3ff66_row50_col2, #T_3ff66_row51_col2, #T_3ff66_row52_col2, #T_3ff66_row53_col2 {\n",
       "  background-color: #f7fbff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row2_col0, #T_3ff66_row6_col0, #T_3ff66_row9_col0, #T_3ff66_row48_col0 {\n",
       "  background-color: #084285;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row3_col0, #T_3ff66_row7_col0, #T_3ff66_row8_col0, #T_3ff66_row20_col0 {\n",
       "  background-color: #083573;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row3_col1, #T_3ff66_row7_col1, #T_3ff66_row8_col1, #T_3ff66_row17_col1 {\n",
       "  background-color: #f1f7fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row4_col0, #T_3ff66_row5_col0, #T_3ff66_row10_col0, #T_3ff66_row11_col0, #T_3ff66_row18_col0, #T_3ff66_row39_col1 {\n",
       "  background-color: #08306b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row12_col0, #T_3ff66_row28_col0, #T_3ff66_row42_col0 {\n",
       "  background-color: #1764ab;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row12_col1, #T_3ff66_row29_col1 {\n",
       "  background-color: #dae8f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row13_col1, #T_3ff66_row14_col1, #T_3ff66_row19_col1, #T_3ff66_row47_col1 {\n",
       "  background-color: #eef5fc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row15_col0, #T_3ff66_row26_col0, #T_3ff66_row37_col1 {\n",
       "  background-color: #206fb4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row16_col0, #T_3ff66_row29_col0 {\n",
       "  background-color: #084d96;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row16_col1, #T_3ff66_row20_col1 {\n",
       "  background-color: #f4f9fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row17_col0, #T_3ff66_row50_col0, #T_3ff66_row53_col0 {\n",
       "  background-color: #08478d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row21_col0 {\n",
       "  background-color: #9dcae1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row21_col1 {\n",
       "  background-color: #2474b7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row22_col0 {\n",
       "  background-color: #4292c6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row22_col1 {\n",
       "  background-color: #63a8d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row23_col0 {\n",
       "  background-color: #3787c0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row23_col1, #T_3ff66_row33_col1 {\n",
       "  background-color: #97c6df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row24_col0, #T_3ff66_row27_col0, #T_3ff66_row31_col0 {\n",
       "  background-color: #2575b7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row24_col1, #T_3ff66_row32_col1, #T_3ff66_row51_col1 {\n",
       "  background-color: #bdd7ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row25_col0, #T_3ff66_row30_col1 {\n",
       "  background-color: #3181bd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row25_col1 {\n",
       "  background-color: #89bedc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row26_col1 {\n",
       "  background-color: #aed1e7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row27_col1 {\n",
       "  background-color: #b8d5ea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row28_col1 {\n",
       "  background-color: #c2d9ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row30_col0 {\n",
       "  background-color: #abd0e6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row31_col1 {\n",
       "  background-color: #a3cce3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row32_col0, #T_3ff66_row51_col0 {\n",
       "  background-color: #125ea6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row33_col0 {\n",
       "  background-color: #3d8dc4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row34_col0, #T_3ff66_row35_col0, #T_3ff66_row52_col0 {\n",
       "  background-color: #1b69af;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row34_col1, #T_3ff66_row43_col1 {\n",
       "  background-color: #cde0f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row35_col1 {\n",
       "  background-color: #d0e2f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row36_col0, #T_3ff66_row52_col1 {\n",
       "  background-color: #c7dcef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row36_col1 {\n",
       "  background-color: #084a91;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row37_col0, #T_3ff66_row40_col0 {\n",
       "  background-color: #79b5d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row38_col0 {\n",
       "  background-color: #58a1cf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row38_col1 {\n",
       "  background-color: #4896c8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row40_col1 {\n",
       "  background-color: #2d7dbb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row41_col0 {\n",
       "  background-color: #6fb0d7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row41_col1 {\n",
       "  background-color: #2979b9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row42_col1, #T_3ff66_row46_col1 {\n",
       "  background-color: #d3e4f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row43_col0 {\n",
       "  background-color: #0e58a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row44_col0, #T_3ff66_row45_col0, #T_3ff66_row46_col0 {\n",
       "  background-color: #0a539e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3ff66_row44_col1 {\n",
       "  background-color: #d7e6f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row45_col1 {\n",
       "  background-color: #ddeaf7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row50_col1 {\n",
       "  background-color: #e0ecf8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3ff66_row53_col1 {\n",
       "  background-color: #e3eef9;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_3ff66\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_3ff66_level0_col0\" class=\"col_heading level0 col0\" >avg_turns</th>\n",
       "      <th id=\"T_3ff66_level0_col1\" class=\"col_heading level0 col1\" >Correct %</th>\n",
       "      <th id=\"T_3ff66_level0_col2\" class=\"col_heading level0 col2\" >n_tasks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >is_single_tool_task</th>\n",
       "      <th class=\"index_name level1\" >model_name</th>\n",
       "      <th class=\"index_name level2\" >action_mode</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"54\">False</th>\n",
       "      <th id=\"T_3ff66_level1_row0\" class=\"row_heading level1 row0\" rowspan=\"3\">CodeLlama-13b-Instruct-hf</th>\n",
       "      <th id=\"T_3ff66_level2_row0\" class=\"row_heading level2 row0\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row0_col0\" class=\"data row0 col0\" >9.8</td>\n",
       "      <td id=\"T_3ff66_row0_col1\" class=\"data row0 col1\" >4.9%</td>\n",
       "      <td id=\"T_3ff66_row0_col2\" class=\"data row0 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row1\" class=\"row_heading level2 row1\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row1_col0\" class=\"data row1 col0\" >9.8</td>\n",
       "      <td id=\"T_3ff66_row1_col1\" class=\"data row1 col1\" >4.9%</td>\n",
       "      <td id=\"T_3ff66_row1_col2\" class=\"data row1 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row2\" class=\"row_heading level2 row2\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row2_col0\" class=\"data row2 col0\" >9.7</td>\n",
       "      <td id=\"T_3ff66_row2_col1\" class=\"data row2 col1\" >4.9%</td>\n",
       "      <td id=\"T_3ff66_row2_col2\" class=\"data row2 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row3\" class=\"row_heading level1 row3\" rowspan=\"3\">CodeLlama-34b-Instruct-hf</th>\n",
       "      <th id=\"T_3ff66_level2_row3\" class=\"row_heading level2 row3\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row3_col0\" class=\"data row3 col0\" >9.9</td>\n",
       "      <td id=\"T_3ff66_row3_col1\" class=\"data row3 col1\" >2.4%</td>\n",
       "      <td id=\"T_3ff66_row3_col2\" class=\"data row3 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row4\" class=\"row_heading level2 row4\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row4_col0\" class=\"data row4 col0\" >10.0</td>\n",
       "      <td id=\"T_3ff66_row4_col1\" class=\"data row4 col1\" >0.0%</td>\n",
       "      <td id=\"T_3ff66_row4_col2\" class=\"data row4 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row5\" class=\"row_heading level2 row5\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row5_col0\" class=\"data row5 col0\" >10.0</td>\n",
       "      <td id=\"T_3ff66_row5_col1\" class=\"data row5 col1\" >0.0%</td>\n",
       "      <td id=\"T_3ff66_row5_col2\" class=\"data row5 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row6\" class=\"row_heading level1 row6\" rowspan=\"3\">CodeLlama-7b-Instruct-hf</th>\n",
       "      <th id=\"T_3ff66_level2_row6\" class=\"row_heading level2 row6\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row6_col0\" class=\"data row6 col0\" >9.7</td>\n",
       "      <td id=\"T_3ff66_row6_col1\" class=\"data row6 col1\" >4.9%</td>\n",
       "      <td id=\"T_3ff66_row6_col2\" class=\"data row6 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row7\" class=\"row_heading level2 row7\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row7_col0\" class=\"data row7 col0\" >9.9</td>\n",
       "      <td id=\"T_3ff66_row7_col1\" class=\"data row7 col1\" >2.4%</td>\n",
       "      <td id=\"T_3ff66_row7_col2\" class=\"data row7 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row8\" class=\"row_heading level2 row8\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row8_col0\" class=\"data row8 col0\" >9.9</td>\n",
       "      <td id=\"T_3ff66_row8_col1\" class=\"data row8 col1\" >2.4%</td>\n",
       "      <td id=\"T_3ff66_row8_col2\" class=\"data row8 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row9\" class=\"row_heading level1 row9\" rowspan=\"3\">Llama-2-13b-chat-hf</th>\n",
       "      <th id=\"T_3ff66_level2_row9\" class=\"row_heading level2 row9\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row9_col0\" class=\"data row9 col0\" >9.7</td>\n",
       "      <td id=\"T_3ff66_row9_col1\" class=\"data row9 col1\" >0.0%</td>\n",
       "      <td id=\"T_3ff66_row9_col2\" class=\"data row9 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row10\" class=\"row_heading level2 row10\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row10_col0\" class=\"data row10 col0\" >10.0</td>\n",
       "      <td id=\"T_3ff66_row10_col1\" class=\"data row10 col1\" >0.0%</td>\n",
       "      <td id=\"T_3ff66_row10_col2\" class=\"data row10 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row11\" class=\"row_heading level2 row11\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row11_col0\" class=\"data row11 col0\" >10.0</td>\n",
       "      <td id=\"T_3ff66_row11_col1\" class=\"data row11 col1\" >0.0%</td>\n",
       "      <td id=\"T_3ff66_row11_col2\" class=\"data row11 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row12\" class=\"row_heading level1 row12\" rowspan=\"3\">Llama-2-70b-chat-hf</th>\n",
       "      <th id=\"T_3ff66_level2_row12\" class=\"row_heading level2 row12\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row12_col0\" class=\"data row12 col0\" >9.1</td>\n",
       "      <td id=\"T_3ff66_row12_col1\" class=\"data row12 col1\" >11.0%</td>\n",
       "      <td id=\"T_3ff66_row12_col2\" class=\"data row12 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row13\" class=\"row_heading level2 row13\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row13_col0\" class=\"data row13 col0\" >9.8</td>\n",
       "      <td id=\"T_3ff66_row13_col1\" class=\"data row13 col1\" >3.7%</td>\n",
       "      <td id=\"T_3ff66_row13_col2\" class=\"data row13 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row14\" class=\"row_heading level2 row14\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row14_col0\" class=\"data row14 col0\" >9.8</td>\n",
       "      <td id=\"T_3ff66_row14_col1\" class=\"data row14 col1\" >3.7%</td>\n",
       "      <td id=\"T_3ff66_row14_col2\" class=\"data row14 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row15\" class=\"row_heading level1 row15\" rowspan=\"3\">Llama-2-7b-chat-hf</th>\n",
       "      <th id=\"T_3ff66_level2_row15\" class=\"row_heading level2 row15\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row15_col0\" class=\"data row15 col0\" >8.9</td>\n",
       "      <td id=\"T_3ff66_row15_col1\" class=\"data row15 col1\" >0.0%</td>\n",
       "      <td id=\"T_3ff66_row15_col2\" class=\"data row15 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row16\" class=\"row_heading level2 row16\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row16_col0\" class=\"data row16 col0\" >9.5</td>\n",
       "      <td id=\"T_3ff66_row16_col1\" class=\"data row16 col1\" >1.2%</td>\n",
       "      <td id=\"T_3ff66_row16_col2\" class=\"data row16 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row17\" class=\"row_heading level2 row17\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row17_col0\" class=\"data row17 col0\" >9.6</td>\n",
       "      <td id=\"T_3ff66_row17_col1\" class=\"data row17 col1\" >2.4%</td>\n",
       "      <td id=\"T_3ff66_row17_col2\" class=\"data row17 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row18\" class=\"row_heading level1 row18\" rowspan=\"3\">Mistral-7B-Instruct-v0.1</th>\n",
       "      <th id=\"T_3ff66_level2_row18\" class=\"row_heading level2 row18\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row18_col0\" class=\"data row18 col0\" >10.0</td>\n",
       "      <td id=\"T_3ff66_row18_col1\" class=\"data row18 col1\" >0.0%</td>\n",
       "      <td id=\"T_3ff66_row18_col2\" class=\"data row18 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row19\" class=\"row_heading level2 row19\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row19_col0\" class=\"data row19 col0\" >9.8</td>\n",
       "      <td id=\"T_3ff66_row19_col1\" class=\"data row19 col1\" >3.7%</td>\n",
       "      <td id=\"T_3ff66_row19_col2\" class=\"data row19 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row20\" class=\"row_heading level2 row20\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row20_col0\" class=\"data row20 col0\" >9.9</td>\n",
       "      <td id=\"T_3ff66_row20_col1\" class=\"data row20 col1\" >1.2%</td>\n",
       "      <td id=\"T_3ff66_row20_col2\" class=\"data row20 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row21\" class=\"row_heading level1 row21\" rowspan=\"3\">claude-2</th>\n",
       "      <th id=\"T_3ff66_level2_row21\" class=\"row_heading level2 row21\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row21_col0\" class=\"data row21 col0\" >7.2</td>\n",
       "      <td id=\"T_3ff66_row21_col1\" class=\"data row21 col1\" >54.9%</td>\n",
       "      <td id=\"T_3ff66_row21_col2\" class=\"data row21 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row22\" class=\"row_heading level2 row22\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row22_col0\" class=\"data row22 col0\" >8.3</td>\n",
       "      <td id=\"T_3ff66_row22_col1\" class=\"data row22 col1\" >39.0%</td>\n",
       "      <td id=\"T_3ff66_row22_col2\" class=\"data row22 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row23\" class=\"row_heading level2 row23\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row23_col0\" class=\"data row23 col0\" >8.5</td>\n",
       "      <td id=\"T_3ff66_row23_col1\" class=\"data row23 col1\" >29.3%</td>\n",
       "      <td id=\"T_3ff66_row23_col2\" class=\"data row23 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row24\" class=\"row_heading level1 row24\" rowspan=\"3\">claude-instant-1</th>\n",
       "      <th id=\"T_3ff66_level2_row24\" class=\"row_heading level2 row24\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row24_col0\" class=\"data row24 col0\" >8.8</td>\n",
       "      <td id=\"T_3ff66_row24_col1\" class=\"data row24 col1\" >20.7%</td>\n",
       "      <td id=\"T_3ff66_row24_col2\" class=\"data row24 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row25\" class=\"row_heading level2 row25\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row25_col0\" class=\"data row25 col0\" >8.6</td>\n",
       "      <td id=\"T_3ff66_row25_col1\" class=\"data row25 col1\" >31.7%</td>\n",
       "      <td id=\"T_3ff66_row25_col2\" class=\"data row25 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row26\" class=\"row_heading level2 row26\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row26_col0\" class=\"data row26 col0\" >8.9</td>\n",
       "      <td id=\"T_3ff66_row26_col1\" class=\"data row26 col1\" >24.4%</td>\n",
       "      <td id=\"T_3ff66_row26_col2\" class=\"data row26 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row27\" class=\"row_heading level1 row27\" rowspan=\"3\">gemini-pro</th>\n",
       "      <th id=\"T_3ff66_level2_row27\" class=\"row_heading level2 row27\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row27_col0\" class=\"data row27 col0\" >8.8</td>\n",
       "      <td id=\"T_3ff66_row27_col1\" class=\"data row27 col1\" >22.0%</td>\n",
       "      <td id=\"T_3ff66_row27_col2\" class=\"data row27 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row28\" class=\"row_heading level2 row28\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row28_col0\" class=\"data row28 col0\" >9.1</td>\n",
       "      <td id=\"T_3ff66_row28_col1\" class=\"data row28 col1\" >19.5%</td>\n",
       "      <td id=\"T_3ff66_row28_col2\" class=\"data row28 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row29\" class=\"row_heading level2 row29\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row29_col0\" class=\"data row29 col0\" >9.5</td>\n",
       "      <td id=\"T_3ff66_row29_col1\" class=\"data row29 col1\" >11.0%</td>\n",
       "      <td id=\"T_3ff66_row29_col2\" class=\"data row29 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row30\" class=\"row_heading level1 row30\" rowspan=\"3\">gpt-3.5-turbo-0613</th>\n",
       "      <th id=\"T_3ff66_level2_row30\" class=\"row_heading level2 row30\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row30_col0\" class=\"data row30 col0\" >7.0</td>\n",
       "      <td id=\"T_3ff66_row30_col1\" class=\"data row30 col1\" >51.2%</td>\n",
       "      <td id=\"T_3ff66_row30_col2\" class=\"data row30 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row31\" class=\"row_heading level2 row31\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row31_col0\" class=\"data row31 col0\" >8.8</td>\n",
       "      <td id=\"T_3ff66_row31_col1\" class=\"data row31 col1\" >26.8%</td>\n",
       "      <td id=\"T_3ff66_row31_col2\" class=\"data row31 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row32\" class=\"row_heading level2 row32\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row32_col0\" class=\"data row32 col0\" >9.2</td>\n",
       "      <td id=\"T_3ff66_row32_col1\" class=\"data row32 col1\" >20.7%</td>\n",
       "      <td id=\"T_3ff66_row32_col2\" class=\"data row32 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row33\" class=\"row_heading level1 row33\" rowspan=\"3\">gpt-3.5-turbo-1106</th>\n",
       "      <th id=\"T_3ff66_level2_row33\" class=\"row_heading level2 row33\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row33_col0\" class=\"data row33 col0\" >8.4</td>\n",
       "      <td id=\"T_3ff66_row33_col1\" class=\"data row33 col1\" >29.3%</td>\n",
       "      <td id=\"T_3ff66_row33_col2\" class=\"data row33 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row34\" class=\"row_heading level2 row34\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row34_col0\" class=\"data row34 col0\" >9.0</td>\n",
       "      <td id=\"T_3ff66_row34_col1\" class=\"data row34 col1\" >15.9%</td>\n",
       "      <td id=\"T_3ff66_row34_col2\" class=\"data row34 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row35\" class=\"row_heading level2 row35\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row35_col0\" class=\"data row35 col0\" >9.0</td>\n",
       "      <td id=\"T_3ff66_row35_col1\" class=\"data row35 col1\" >14.6%</td>\n",
       "      <td id=\"T_3ff66_row35_col2\" class=\"data row35 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row36\" class=\"row_heading level1 row36\" rowspan=\"3\">gpt-4-0613</th>\n",
       "      <th id=\"T_3ff66_level2_row36\" class=\"row_heading level2 row36\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row36_col0\" class=\"data row36 col0\" >6.6</td>\n",
       "      <td id=\"T_3ff66_row36_col1\" class=\"data row36 col1\" >67.1%</td>\n",
       "      <td id=\"T_3ff66_row36_col2\" class=\"data row36 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row37\" class=\"row_heading level2 row37\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row37_col0\" class=\"data row37 col0\" >7.6</td>\n",
       "      <td id=\"T_3ff66_row37_col1\" class=\"data row37 col1\" >56.1%</td>\n",
       "      <td id=\"T_3ff66_row37_col2\" class=\"data row37 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row38\" class=\"row_heading level2 row38\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row38_col0\" class=\"data row38 col0\" >8.0</td>\n",
       "      <td id=\"T_3ff66_row38_col1\" class=\"data row38 col1\" >45.1%</td>\n",
       "      <td id=\"T_3ff66_row38_col2\" class=\"data row38 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row39\" class=\"row_heading level1 row39\" rowspan=\"3\">gpt-4-1106-preview</th>\n",
       "      <th id=\"T_3ff66_level2_row39\" class=\"row_heading level2 row39\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row39_col0\" class=\"data row39 col0\" >5.5</td>\n",
       "      <td id=\"T_3ff66_row39_col1\" class=\"data row39 col1\" >74.4%</td>\n",
       "      <td id=\"T_3ff66_row39_col2\" class=\"data row39 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row40\" class=\"row_heading level2 row40\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row40_col0\" class=\"data row40 col0\" >7.6</td>\n",
       "      <td id=\"T_3ff66_row40_col1\" class=\"data row40 col1\" >52.4%</td>\n",
       "      <td id=\"T_3ff66_row40_col2\" class=\"data row40 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row41\" class=\"row_heading level2 row41\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row41_col0\" class=\"data row41 col0\" >7.7</td>\n",
       "      <td id=\"T_3ff66_row41_col1\" class=\"data row41 col1\" >53.7%</td>\n",
       "      <td id=\"T_3ff66_row41_col2\" class=\"data row41 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row42\" class=\"row_heading level1 row42\" rowspan=\"3\">lemur-70b-chat-v1</th>\n",
       "      <th id=\"T_3ff66_level2_row42\" class=\"row_heading level2 row42\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row42_col0\" class=\"data row42 col0\" >9.1</td>\n",
       "      <td id=\"T_3ff66_row42_col1\" class=\"data row42 col1\" >13.4%</td>\n",
       "      <td id=\"T_3ff66_row42_col2\" class=\"data row42 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row43\" class=\"row_heading level2 row43\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row43_col0\" class=\"data row43 col0\" >9.3</td>\n",
       "      <td id=\"T_3ff66_row43_col1\" class=\"data row43 col1\" >15.9%</td>\n",
       "      <td id=\"T_3ff66_row43_col2\" class=\"data row43 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row44\" class=\"row_heading level2 row44\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row44_col0\" class=\"data row44 col0\" >9.4</td>\n",
       "      <td id=\"T_3ff66_row44_col1\" class=\"data row44 col1\" >12.2%</td>\n",
       "      <td id=\"T_3ff66_row44_col2\" class=\"data row44 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row45\" class=\"row_heading level1 row45\" rowspan=\"3\">mint-agent</th>\n",
       "      <th id=\"T_3ff66_level2_row45\" class=\"row_heading level2 row45\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row45_col0\" class=\"data row45 col0\" >9.4</td>\n",
       "      <td id=\"T_3ff66_row45_col1\" class=\"data row45 col1\" >9.8%</td>\n",
       "      <td id=\"T_3ff66_row45_col2\" class=\"data row45 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row46\" class=\"row_heading level2 row46\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row46_col0\" class=\"data row46 col0\" >9.4</td>\n",
       "      <td id=\"T_3ff66_row46_col1\" class=\"data row46 col1\" >13.4%</td>\n",
       "      <td id=\"T_3ff66_row46_col2\" class=\"data row46 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row47\" class=\"row_heading level2 row47\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row47_col0\" class=\"data row47 col0\" >9.8</td>\n",
       "      <td id=\"T_3ff66_row47_col1\" class=\"data row47 col1\" >3.7%</td>\n",
       "      <td id=\"T_3ff66_row47_col2\" class=\"data row47 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row48\" class=\"row_heading level1 row48\" rowspan=\"3\">text-davinci-002</th>\n",
       "      <th id=\"T_3ff66_level2_row48\" class=\"row_heading level2 row48\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row48_col0\" class=\"data row48 col0\" >9.7</td>\n",
       "      <td id=\"T_3ff66_row48_col1\" class=\"data row48 col1\" >4.9%</td>\n",
       "      <td id=\"T_3ff66_row48_col2\" class=\"data row48 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row49\" class=\"row_heading level2 row49\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row49_col0\" class=\"data row49 col0\" >9.8</td>\n",
       "      <td id=\"T_3ff66_row49_col1\" class=\"data row49 col1\" >4.9%</td>\n",
       "      <td id=\"T_3ff66_row49_col2\" class=\"data row49 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row50\" class=\"row_heading level2 row50\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row50_col0\" class=\"data row50 col0\" >9.6</td>\n",
       "      <td id=\"T_3ff66_row50_col1\" class=\"data row50 col1\" >8.5%</td>\n",
       "      <td id=\"T_3ff66_row50_col2\" class=\"data row50 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level1_row51\" class=\"row_heading level1 row51\" rowspan=\"3\">text-davinci-003</th>\n",
       "      <th id=\"T_3ff66_level2_row51\" class=\"row_heading level2 row51\" >code_as_action</th>\n",
       "      <td id=\"T_3ff66_row51_col0\" class=\"data row51 col0\" >9.2</td>\n",
       "      <td id=\"T_3ff66_row51_col1\" class=\"data row51 col1\" >20.7%</td>\n",
       "      <td id=\"T_3ff66_row51_col2\" class=\"data row51 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row52\" class=\"row_heading level2 row52\" >json_as_action</th>\n",
       "      <td id=\"T_3ff66_row52_col0\" class=\"data row52 col0\" >9.0</td>\n",
       "      <td id=\"T_3ff66_row52_col1\" class=\"data row52 col1\" >18.3%</td>\n",
       "      <td id=\"T_3ff66_row52_col2\" class=\"data row52 col2\" >82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3ff66_level2_row53\" class=\"row_heading level2 row53\" >text_as_action</th>\n",
       "      <td id=\"T_3ff66_row53_col0\" class=\"data row53 col0\" >9.6</td>\n",
       "      <td id=\"T_3ff66_row53_col1\" class=\"data row53 col1\" >7.3%</td>\n",
       "      <td id=\"T_3ff66_row53_col2\" class=\"data row53 col2\" >82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fb8711967c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats, output_df = analyze_dir([\n",
    "    'travel_itinerary_planning',\n",
    "    'dna_sequencer',\n",
    "    'message_decoder',\n",
    "    'trade_calculator',\n",
    "    'web_browsing'\n",
    "    ], \n",
    "    prefix=\"outputs/\"\n",
    "    # prefix=\"outputs.old.v4.before-contain-word/\"\n",
    "    # prefix=\"outputs.old.v5.with-thought/\"\n",
    ")\n",
    "stats = stats.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Mode with highest correct %</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Mode with lowest turns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>code_as_action</th>\n",
       "      <th>json_as_action</th>\n",
       "      <th>text_as_action</th>\n",
       "      <th>code_as_action</th>\n",
       "      <th>json_as_action</th>\n",
       "      <th>text_as_action</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mode with highest correct %                                \\\n",
       "               code_as_action json_as_action text_as_action   \n",
       "0                          12              4              2   \n",
       "\n",
       "  Mode with lowest turns                                \n",
       "          code_as_action json_as_action text_as_action  \n",
       "0                     13              3              2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllllll}\n",
      "\\toprule\n",
      "{} & \\multicolumn{3}{l}{Mode with highest correct %} & \\multicolumn{3}{l}{Mode with lowest turns} \\\\\n",
      "{} &              code_as_action &   json_as_action & text_as_action &         code_as_action &   json_as_action & text_as_action \\\\\n",
      "\\midrule\n",
      "0 &               $\\mathbf{12}$ &  \\underline{$4$} &            $2$ &          $\\mathbf{13}$ &  \\underline{$3$} &            $2$ \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "=============\n",
      "\\begin{tabular}{lllllll}\n",
      "\\toprule\n",
      "{} & \\multicolumn{3}{l}{1Correct %} & \\multicolumn{3}{l}{2Avg. Turns} \\\\\n",
      "action_mode &       code_as_action &       json_as_action &       text_as_action &      code_as_action &       json_as_action &       text_as_action \\\\\n",
      "model_name                         &                      &                      &                      &                     &                      &                      \\\\\n",
      "\\midrule\n",
      "\\texttt{CodeLlama-13b-Instruct-hf} &      $\\mathbf{4.90}$ &      $\\mathbf{4.90}$ &      $\\mathbf{4.90}$ &  \\underline{$9.80$} &   \\underline{$9.80$} &      $\\mathbf{9.70}$ \\\\\n",
      "\\texttt{CodeLlama-34b-Instruct-hf} &      $\\mathbf{2.40}$ &   \\underline{$0.00$} &   \\underline{$0.00$} &     $\\mathbf{9.90}$ &  \\underline{$10.00$} &  \\underline{$10.00$} \\\\\n",
      "\\texttt{CodeLlama-7b-Instruct-hf}  &      $\\mathbf{4.90}$ &   \\underline{$2.40$} &   \\underline{$2.40$} &     $\\mathbf{9.70}$ &   \\underline{$9.90$} &   \\underline{$9.90$} \\\\\n",
      "\\texttt{Llama-2-13b-chat-hf}       &      $\\mathbf{0.00}$ &      $\\mathbf{0.00}$ &      $\\mathbf{0.00}$ &     $\\mathbf{9.70}$ &  \\underline{$10.00$} &  \\underline{$10.00$} \\\\\n",
      "\\texttt{Llama-2-70b-chat-hf}       &     $\\mathbf{11.00}$ &   \\underline{$3.70$} &   \\underline{$3.70$} &     $\\mathbf{9.10}$ &   \\underline{$9.80$} &   \\underline{$9.80$} \\\\\n",
      "\\texttt{Llama-2-7b-chat-hf}        &               $0.00$ &   \\underline{$1.20$} &      $\\mathbf{2.40}$ &     $\\mathbf{8.90}$ &   \\underline{$9.50$} &               $9.60$ \\\\\n",
      "\\texttt{Mistral-7B-Instruct-v0.1}  &               $0.00$ &      $\\mathbf{3.70}$ &   \\underline{$1.20$} &             $10.00$ &      $\\mathbf{9.80}$ &   \\underline{$9.90$} \\\\\n",
      "\\texttt{claude-2}                  &     $\\mathbf{54.90}$ &  \\underline{$39.00$} &              $29.30$ &     $\\mathbf{7.20}$ &   \\underline{$8.30$} &               $8.50$ \\\\\n",
      "\\texttt{claude-instant-1}          &              $20.70$ &     $\\mathbf{31.70}$ &  \\underline{$24.40$} &  \\underline{$8.80$} &      $\\mathbf{8.60}$ &               $8.90$ \\\\\n",
      "\\texttt{gemini-pro}                &     $\\mathbf{22.00}$ &  \\underline{$19.50$} &              $11.00$ &     $\\mathbf{8.80}$ &   \\underline{$9.10$} &               $9.50$ \\\\\n",
      "\\texttt{gpt-3.5-turbo-0613}        &     $\\mathbf{51.20}$ &  \\underline{$26.80$} &              $20.70$ &     $\\mathbf{7.00}$ &   \\underline{$8.80$} &               $9.20$ \\\\\n",
      "\\texttt{gpt-3.5-turbo-1106}        &     $\\mathbf{29.30}$ &  \\underline{$15.90$} &              $14.60$ &     $\\mathbf{8.40}$ &   \\underline{$9.00$} &   \\underline{$9.00$} \\\\\n",
      "\\texttt{gpt-4-0613}                &     $\\mathbf{67.10}$ &  \\underline{$56.10$} &              $45.10$ &     $\\mathbf{6.60}$ &   \\underline{$7.60$} &               $8.00$ \\\\\n",
      "\\texttt{gpt-4-1106-preview}        &     $\\mathbf{74.40}$ &              $52.40$ &  \\underline{$53.70$} &     $\\mathbf{5.50}$ &   \\underline{$7.60$} &               $7.70$ \\\\\n",
      "\\texttt{lemur-70b-chat-v1}         &  \\underline{$13.40$} &     $\\mathbf{15.90}$ &              $12.20$ &     $\\mathbf{9.10}$ &   \\underline{$9.30$} &               $9.40$ \\\\\n",
      "\\texttt{mint-agent}                &   \\underline{$9.80$} &     $\\mathbf{13.40}$ &               $3.70$ &     $\\mathbf{9.40}$ &      $\\mathbf{9.40}$ &               $9.80$ \\\\\n",
      "\\texttt{text-davinci-002}          &   \\underline{$4.90$} &   \\underline{$4.90$} &      $\\mathbf{8.50}$ &  \\underline{$9.70$} &               $9.80$ &      $\\mathbf{9.60}$ \\\\\n",
      "\\texttt{text-davinci-003}          &     $\\mathbf{20.70}$ &  \\underline{$18.30$} &               $7.30$ &  \\underline{$9.20$} &      $\\mathbf{9.00}$ &               $9.60$ \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1891771/1154700701.py:40: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  print(_combined_stats.to_latex(escape=False))\n",
      "/tmp/ipykernel_1891771/1154700701.py:53: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  print(_viz_latex.to_latex(escape=False))\n"
     ]
    }
   ],
   "source": [
    "_viz_latex = stats.reset_index()\n",
    "assert _viz_latex[\"n_tasks\"].nunique() == 1\n",
    "assert _viz_latex[\"is_single_tool_task\"].nunique() == 1\n",
    "_viz_latex[\"Correct %\"] = (_viz_latex[\"Correct %\"] * 100).round(2)\n",
    "_viz_latex[\"model_name\"] = _viz_latex[\"model_name\"].apply(lambda x: f\"\\\\texttt{{{x}}}\")\n",
    "def _process_row(row, lower_better=False, formatter=\"{:.2f}\"):\n",
    "    sorted_values = sorted(row, reverse=True)\n",
    "    if lower_better:\n",
    "        sorted_values = sorted(row)\n",
    "    max_value = sorted_values[0]\n",
    "    second_max_value = sorted_values[1]\n",
    "    \n",
    "    def _format_value(value):\n",
    "        formatted_value = formatter.format(value)\n",
    "        if value == max_value:\n",
    "            return '$\\\\mathbf{' + str(formatted_value) + '}$'\n",
    "        elif value == second_max_value:\n",
    "            return '\\\\underline{$' + str(formatted_value) + '$}'\n",
    "        else:\n",
    "            return \"$\" + str(formatted_value) + \"$\"\n",
    "    row = row.apply(_format_value)\n",
    "    return row\n",
    "\n",
    "_viz_latex = _viz_latex.set_index([\"model_name\", \"action_mode\"])[['avg_turns', 'Correct %']].unstack()\n",
    "\n",
    "# display(_viz_latex)\n",
    "# find the argmax for correct %\n",
    "\n",
    "argmax_correct = _viz_latex[\"Correct %\"].idxmax(axis=1)\n",
    "max_mode_count = argmax_correct.value_counts()\n",
    "arg_lowest_turns = _viz_latex[\"avg_turns\"].idxmin(axis=1)\n",
    "lowest_turns_mode_count = arg_lowest_turns.value_counts()\n",
    "# combine the two\n",
    "_combined_stats = pd.concat(\n",
    "    [max_mode_count, lowest_turns_mode_count], axis=1, keys=[\"Mode with highest correct %\", \"Mode with lowest turns\"])\n",
    "_combined_stats = _combined_stats.unstack().to_frame().T\n",
    "display(_combined_stats)\n",
    "_combined_stats[\"Mode with highest correct %\"] = _combined_stats[\"Mode with highest correct %\"].apply(_process_row, axis=1, formatter=\"{:d}\")\n",
    "_combined_stats[\"Mode with lowest turns\"] = _combined_stats[\"Mode with lowest turns\"].apply(_process_row, axis=1, formatter=\"{:d}\")\n",
    "print(_combined_stats.to_latex(escape=False))\n",
    "\n",
    "\n",
    "print(\"=============\")\n",
    "_viz_latex[\"Correct %\"] = _viz_latex[\"Correct %\"].apply(_process_row, axis=1)\n",
    "_viz_latex[\"avg_turns\"] = _viz_latex[\"avg_turns\"].apply(_process_row, axis=1, lower_better=True)\n",
    "_viz_latex = _viz_latex.rename(\n",
    "    columns={\n",
    "        \"avg_turns\": \"2Avg. Turns\",\n",
    "        \"Correct %\": \"1Correct %\"\n",
    "    }\n",
    ").sort_index(axis=1)\n",
    "# _viz_latex = _viz_latex.swaplevel(axis=1).sort_index(axis=1)\n",
    "print(_viz_latex.to_latex(escape=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2070.75x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "_viz_df = stats.query(\"n_tasks == 82 and is_single_tool_task == False\")\n",
    "_viz_df = _viz_df.reset_index().drop(columns=[\"is_single_tool_task\"]).set_index([\"model_name\", \"action_mode\"])\n",
    "_viz_df[\"Correct %\"] = _viz_df[\"Correct %\"] * 100\n",
    "\n",
    "def visualize_model_performance_seaborn(dataframe, fig_height=6):\n",
    "    # Check for required columns\n",
    "    required_columns = {'model_name', 'action_mode', 'avg_turns', 'Correct %'}\n",
    "    if not required_columns.issubset(dataframe.columns):\n",
    "        raise ValueError(f\"The DataFrame must contain the following columns: {required_columns}\")\n",
    "\n",
    "    # Sorting the DataFrame\n",
    "    sorted_models = dataframe[\n",
    "        dataframe['action_mode'] == 'code_as_action'\n",
    "    ].sort_values(by='Correct %', ascending=False)['model_name'].unique()\n",
    "    dataframe['model_rank'] = dataframe['model_name'].apply(lambda x: np.where(sorted_models == x)[0][0])\n",
    "\n",
    "    # rename action model for visualization\n",
    "    dataframe[\"action_mode\"] = dataframe[\"action_mode\"].replace({\n",
    "        \"code_as_action\": \"Code as Action\",\n",
    "        \"json_as_action\": \"JSON as Action\",\n",
    "        \"text_as_action\": \"Text as Action\"\n",
    "    })\n",
    "\n",
    "    # Custom color palette\n",
    "    custom_palette = {\n",
    "        # 'code_as_action': '#76c7c0',  # Soft teal/turquoise\n",
    "        # 'json_as_action': '#ff9a8d',  # Muted coral/salmon\n",
    "        # 'text_as_action': '#b8a9c9'   # Light lavender/lilac\n",
    "        'Code as Action': '#76c7c0',  # Soft teal/turquoise\n",
    "        'JSON as Action': '#ff9a8d',  # Muted coral/salmon\n",
    "        'Text as Action': '#b8a9c9'   # Light lavender/lilac\n",
    "    }\n",
    "\n",
    "    # Melt the DataFrame for seaborn compatibility\n",
    "    df_melted = dataframe.melt(id_vars=['model_name', 'model_rank', 'action_mode'], \n",
    "                               value_vars=['Correct %', 'avg_turns'],\n",
    "                               var_name='metric', value_name='value')\n",
    "\n",
    "    # Create a FacetGrid for different metrics\n",
    "    g = sns.FacetGrid(df_melted, col='metric', sharex=False, height=fig_height, aspect=1.5)\n",
    "    \n",
    "    # Map the barplot with the custom palette\n",
    "    g = g.map_dataframe(sns.barplot, x='value', y='model_rank', hue='action_mode', orient='h', palette=custom_palette)\n",
    "\n",
    "    # Drawing lines considering the offset\n",
    "    action_modes = dataframe['action_mode'].unique()\n",
    "    n_modes = len(action_modes)\n",
    "    bar_width = 0.2\n",
    "    offsets = np.linspace(-bar_width*n_modes/2, bar_width*n_modes/2, n_modes)\n",
    "\n",
    "    for ax, metric in zip(g.axes.flat, ['Correct %', 'avg_turns']):\n",
    "        for offset, action_mode in zip(offsets, action_modes):\n",
    "            subset = df_melted[(df_melted['action_mode'] == action_mode) & (df_melted['metric'] == metric)]\n",
    "            subset = subset.sort_values('model_rank')\n",
    "            ax.plot(\n",
    "                subset['value'],\n",
    "                subset['model_rank'] + offset,\n",
    "                linestyle='dashed',\n",
    "                color=custom_palette[action_mode],\n",
    "                linewidth=2\n",
    "            )\n",
    "\n",
    "\n",
    "    # Adjust the legend and axis\n",
    "    for ax, title in zip(g.axes.flat, ['Success Rate (%)', 'Average Number of Interaction Turns']):\n",
    "        ax.set_title(title)\n",
    "        # ax.set_xlabel('Average Number of Interaction Turns' if title == 'Average Interaction Turns' else 'Success Rate (%)')\n",
    "        ax.set_xlabel(\"\")\n",
    "        ax.set_yticks(range(len(sorted_models)))\n",
    "        ax.set_yticklabels(sorted_models)\n",
    "\n",
    "    # Set the x-axis range for the 'Average Interaction Turns' plot\n",
    "    g.axes.flat[1].set_xlim(5, 10)  # Adjust as needed\n",
    "    # remove y-axis label\n",
    "    g.axes.flat[0].set_ylabel('')\n",
    "    # Adjust layout and place legend at the bottom\n",
    "    g.add_legend(title='Action Mode', loc='center', ncol=1, bbox_to_anchor=(0.37, 0.32))\n",
    "    return g\n",
    "\n",
    "# sns.set(font=\"Roboto\")\n",
    "# sns.set_style({'font.family': 'Roboto Mono'})\n",
    "\n",
    "_selected_models = [\n",
    "    \"gpt-4-1106-preview\",\n",
    "    \"gpt-4-0613\",\n",
    "    \"claude-2\",\n",
    "    \"gpt-3.5-turbo-0613\",\n",
    "    \"gpt-3.5-turbo-1106\",\n",
    "    \"gemini-pro\",\n",
    "    \"text-davinci-003\",\n",
    "    \"Llama-2-70b-chat-hf\"\n",
    "]\n",
    "\n",
    "with sns.axes_style(\"white\"), sns.plotting_context(\"paper\", font_scale=2):\n",
    "    _fig = visualize_model_performance_seaborn(\n",
    "        _viz_df.reset_index().set_index(\"model_name\").loc[_selected_models].reset_index(),\n",
    "        fig_height=6\n",
    "    )\n",
    "# _fig.savefig(\"plots/zeroshot_act_model_performance.pdf\", bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "llm-agent",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
